Tools for using spatial data within R
Cindy Traub, Ph.D.
3/9/2017
Mapping Metrolink and STL Metro bus routes
Inspired by the maps on http://jkunst.com/r/plotting-gtfs-data-with-r/, I set out to adapt the code and map the Metro system in St. Louis, Missouri.
The site https://www.metrostlouis.org/developer-resources/ contains links to the General Transit Specification Feed (GTFS) Data for Metro Transit - St. Louis. The zip file located at the link for “Metro Transit â St. Louis GTFS Data Feed” downloaded March 6, 2017, contains the files:
- agency.txt
- calendar_dates.txt
- calendar.txt
- routes.txt
- shapes.txt
- stop_times.txt
- stops.txt
- trips.txt
From this, stops.txt and shapes.txt will allow us to draw out the routes of both Metro bus and Metrolink lines.
# Update the location of the STL GTFS files
STLmapPath<- "C:/Users/cindytraub/Box Sync/CT_presentations/MeetupSpatialR/gtfs_STL/"
# STLmapPath<- "/Users/CTatWUSTL/Box Sync/CT_presentations/MeetupSpatialR/gtfs_STL/"
# read in the shapes csv
shapes <- fread(paste0(STLmapPath,"shapes.txt")) # Reads in file regardless of working dir
dim(shapes)## [1] 288790 5summary(shapes)## shape_id shape_pt_lat shape_pt_lon shape_pt_sequence
## Min. :91002 Min. :38.33 Min. :-90.68 Min. : 1.0
## 1st Qu.:91210 1st Qu.:38.59 1st Qu.:-90.35 1st Qu.: 109.0
## Median :91559 Median :38.64 Median :-90.29 Median : 235.0
## Mean :91477 Mean :38.64 Mean :-90.27 Mean : 297.6
## 3rd Qu.:91757 3rd Qu.:38.70 3rd Qu.:-90.21 3rd Qu.: 413.0
## Max. :91916 Max. :38.84 Max. :-89.79 Max. :1925.0
## shape_dist_traveled
## Mode:logical
## NA's:288790
##
##
##
## # examine shapes
head(shapes,10)## shape_id shape_pt_lat shape_pt_lon shape_pt_sequence
## 1: 91228 38.69214 -90.39549 1
## 2: 91228 38.69189 -90.39301 2
## 3: 91228 38.69185 -90.39265 3
## 4: 91228 38.69178 -90.39205 4
## 5: 91228 38.69170 -90.39124 5
## 6: 91228 38.69151 -90.38948 6
## 7: 91228 38.69145 -90.38890 7
## 8: 91228 38.69138 -90.38821 8
## 9: 91228 38.69124 -90.38690 9
## 10: 91228 38.69115 -90.38612 10
## shape_dist_traveled
## 1: NA
## 2: NA
## 3: NA
## 4: NA
## 5: NA
## 6: NA
## 7: NA
## 8: NA
## 9: NA
## 10: NAshapes[, length(unique(shape_id))]## [1] 681shapes[,.N, shape_id]## shape_id N
## 1: 91228 145
## 2: 91317 1169
## 3: 91316 710
## 4: 91088 561
## 5: 91315 82
## ---
## 677: 91277 580
## 678: 91270 57
## 679: 91271 293
## 680: 91272 48
## 681: 91273 255Map all transit routes
p <- ggplot(shapes) +
geom_path(aes(shape_pt_lon, shape_pt_lat, group = shape_id),
size = .1, alpha = .1) +
coord_equal() + theme_map()
p
This gives us an interesting view of major roads in the area, but is pretty heavy in terms of data (see dimensions of shapes data table). It is also distorted, because treating longitude and latitude as \(x\) and \(y\) coordinates for the sake of plotting is not an accurate representation of the physical earth. It works fine for exploratory plotting, however. For a detailed presentation of how to make maps within ggplot using ggmap, see the vignette by Robin Lovelace at https://github.com/Robinlovelace/Creating-maps-in-R/blob/master/vignettes/ggmap.Rmd.
Examine other GTFS files
routes <- fread(paste0(STLmapPath,"routes.txt"))
dim(routes)## [1] 162 7head(routes,10)## route_long_name route_type route_text_color agency_id route_id
## 1: Oakville 3 000000 NA 13519
## 2: City Limits 3 000000 NA 13518
## 3: Hazelwood 3 000000 NA 13636
## 4: Berkeley-Florissant 3 000000 NA 13637
## 5: Rock Road 3 000000 NA 13634
## 6: Hanley-Graham 3 000000 NA 13635
## 7: Dorsett-Lackland 3 000000 NA 13632
## 8: Earth City 3 000000 NA 13633
## 9: Soulard 3 000000 NA 13630
## 10: ML King-Chouteau 3 000000 NA 13631
## route_color route_short_name
## 1: FFFFFF 17
## 2: FFFFFF 16
## 3: FFFFFF 38
## 4: FFFFFF 39
## 5: FFFFFF 35
## 6: FFFFFF 37
## 7: FFFFFF 33
## 8: FFFFFF 34
## 9: FFFFFF 30
## 10: FFFFFF 32summary(routes)## route_long_name route_type route_text_color agency_id
## Length:162 Min. :2.000 Length:162 Mode:logical
## Class :character 1st Qu.:3.000 Class :character NA's:162
## Mode :character Median :3.000 Mode :character
## Mean :2.975
## 3rd Qu.:3.000
## Max. :3.000
## route_id route_color route_short_name
## Length:162 Length:162 Length:162
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
## routes[route_type==2] # note this got the MetroLink routes only## route_long_name route_type route_text_color agency_id route_id
## 1: MetroLink Blue Line 2 FFFFFF NA 13576B
## 2: MetroLink Red Line 2 FFFFFF NA 13576R
## 3: MetroLink Blue Line 2 FFFFFF NA 13684B
## 4: MetroLink Red Line 2 FFFFFF NA 13684R
## route_color route_short_name
## 1: 333399 MLB
## 2: CC0033 MLR
## 3: 333399 MLB
## 4: CC0033 MLRtrips <- fread(paste0(STLmapPath,"trips.txt"))
dim(trips)## [1] 22628 7head(trips,10)## block_id route_id direction_id trip_headsign
## 1: b_69128 13563 0 CAHOKIA - TO 5TH & MO VIA AIRPORT
## 2: b_69131 13563 0 CAHOKIA - TO 5TH & MO VIA AIRPORT
## 3: b_69122 13563 0 CAHOKIA - TO 5TH & MO VIA AIRPORT
## 4: b_69129 13563 0 CAHOKIA - TO 5TH & MO VIA AIRPORT
## 5: b_69128 13563 0 CAHOKIA - TO 5TH & MO VIA AIRPORT
## 6: b_69131 13563 0 CAHOKIA - TO 5TH & MO VIA AIRPORT
## 7: b_69131 13563 0 CAHOKIA - TO 5TH & MO VIA WATER ST
## 8: b_69129 13563 0 CAHOKIA - TO 5TH & MO VIA AIRPORT
## 9: b_69122 13563 0 CAHOKIA - TO 5TH & MO VIA AIRPORT
## 10: b_69129 13563 0 CAHOKIA - TO 5TH & MO VIA AIRPORT
## shape_id service_id trip_id
## 1: 91258 1_merged_2200336 2174276
## 2: 91258 1_merged_2200336 2174277
## 3: 91258 1_merged_2200336 2174274
## 4: 91258 1_merged_2200336 2174275
## 5: 91258 1_merged_2200336 2174272
## 6: 91258 1_merged_2200336 2174273
## 7: 91259 1_merged_2200336 2174270
## 8: 91258 1_merged_2200336 2174271
## 9: 91258 1_merged_2200336 2174278
## 10: 91258 1_merged_2200336 2174279stops <- fread(paste0(STLmapPath,"stops.txt"))
dim(stops)## [1] 7465 8head(stops,10)## stop_lat zone_id stop_lon stop_id
## 1: 38.62636 NA -90.18980 13357
## 2: 38.54872 NA -90.01320 11542
## 3: 38.54871 NA -90.01151 11543
## 4: 38.54880 NA -90.01801 11540
## 5: 38.54873 NA -90.01417 11541
## 6: 38.60284 NA -90.05177 11546
## 7: 38.60848 NA -90.04896 11547
## 8: 38.55633 NA -90.01806 11545
## 9: 38.61090 NA -90.04762 11548
## 10: 38.61269 NA -90.04663 11549
## stop_desc
## 1: NEAR SIDE BROADWAY @ CHESTNUT SB
## 2: NEAR SIDE MEMORIAL @ HIGH FOREST EB
## 3: NEAR SIDE MEMORIAL @ ROYAL HEIGHTS EB
## 4: FAR SIDE MEMORIAL @ 43RD STREET EB
## 5: NEAR SIDE MEMORIAL @ 39TH STREET EB
## 6: FAR SIDE ILLINOIS ROUTE 157 @ CALIFORNIA NB
## 7: NEAR SIDE ILLINOIS ROUTE 157 @ GARDEN NB
## 8: FAR SIDE FLANAGAN @ IDA NB
## 9: FAR SIDE ILLINOIS ROUTE 157 @ TUCKER NB
## 10: NEAR SIDE ILLINOIS ROUTE 157 @ RAUCKMAN NB
## stop_name location_type stop_code
## 1: BROADWAY @ CHESTNUT SB 0 13357
## 2: MEMORIAL @ HIGH FOREST EB 0 11542
## 3: MEMORIAL @ ROYAL HEIGHTS EB 0 11543
## 4: MEMORIAL @ 43RD STREET EB 0 11540
## 5: MEMORIAL @ 39TH STREET EB 0 11541
## 6: ILLINOIS ROUTE 157 @ CALIFORNIA NB 0 11546
## 7: ILLINOIS ROUTE 157 @ GARDEN NB 0 11547
## 8: FLANAGAN @ IDA NB 0 11545
## 9: ILLINOIS ROUTE 157 @ TUCKER NB 0 11548
## 10: ILLINOIS ROUTE 157 @ RAUCKMAN NB 0 11549summary(stops)## stop_lat zone_id stop_lon stop_id
## Min. :38.33 Mode:logical Min. :-90.68 Min. : 6
## 1st Qu.:38.58 NA's:7465 1st Qu.:-90.33 1st Qu.: 5282
## Median :38.63 Median :-90.26 Median : 8854
## Mean :38.63 Mean :-90.24 Mean : 9033
## 3rd Qu.:38.68 3rd Qu.:-90.18 3rd Qu.:12636
## Max. :38.84 Max. :-89.79 Max. :16333
## stop_desc stop_name location_type stop_code
## Length:7465 Length:7465 Min. :0 Min. : 6
## Class :character Class :character 1st Qu.:0 1st Qu.: 5282
## Mode :character Mode :character Median :0 Median : 8854
## Mean :0 Mean : 9033
## 3rd Qu.:0 3rd Qu.:12636
## Max. :0 Max. :16333Where are the Metrolink stops?
head(stops$stop_name,20)## [1] "BROADWAY @ CHESTNUT SB"
## [2] "MEMORIAL @ HIGH FOREST EB"
## [3] "MEMORIAL @ ROYAL HEIGHTS EB"
## [4] "MEMORIAL @ 43RD STREET EB"
## [5] "MEMORIAL @ 39TH STREET EB"
## [6] "ILLINOIS ROUTE 157 @ CALIFORNIA NB"
## [7] "ILLINOIS ROUTE 157 @ GARDEN NB"
## [8] "FLANAGAN @ IDA NB"
## [9] "ILLINOIS ROUTE 157 @ TUCKER NB"
## [10] "ILLINOIS ROUTE 157 @ RAUCKMAN NB"
## [11] "NORTH HANLEY @ MONROE NB"
## [12] "NORTH HANLEY @ ALERT NB"
## [13] "NORTH HANLEY @ MILAN NB"
## [14] "NORTH HANLEY @ ANNA NB"
## [15] "NORTH HANLEY @ CANTON NB"
## [16] "NORTH HANLEY @ WASHINGTON NB"
## [17] "NORTH HANLEY @ PAGE NB"
## [18] "BELLEFONTAINE @ ST CYR NB"
## [19] "BELLEFONTAINE @ ZELLWEGER NB"
## [20] "BELLEFONTAINE @ TEURVILLE NB"stops[grep("METRO", stops$stop_name), stop_name] # some are bus stops## [1] "CONVENTION CENTER METROLINK STATION"
## [2] "METRO PLAZA @ SECTION LINE EB"
## [3] "METRO MAIN SHOP GATE @ SPRUCE NB"
## [4] "DES PERES @ DELMAR LOOP METROLINK WB"
## [5] "ROCK ROAD METROBUS LOOP @ METROLINK"
## [6] "WASHINGTON @ 6TH STREET METROLINK EB"
## [7] "CLAYTON METROBUS CENTER @ METROLINK"
## [8] "METRO PLAZA @ SECTION LINE WB"
## [9] "TAYLOR @ BROADWAY-TAYLOR METROBUS LOOP WB"
## [10] "DEBALIVIERE AVE @ FOREST PARK METROLINK NB"
## [11] "NORTH HANLEY METROBUS LOOP @ METROLINK"
## [12] "UMSL SOUTH METROBUS LOOP @ METROLINK"
## [13] "BRENTWOOD I-64 METROBUS LOOP @ METROLINK"
## [14] "DEBALIVIERE AVE @ FOREST PARK METROLINK SB"
## [15] "SUNNEN METROLINK STATION"
## [16] "MAPLEWOOD METROLINK STATION"
## [17] "BRENTWOOD METROLINK STATION"
## [18] "RICHMOND HEIGHTS METROLINK STATION"
## [19] "SHREWSBURY METROLINK STATION"
## [20] "EMERSON PARK METROLINK STATION"
## [21] "BALLAS METROBUS CENTER"
## [22] "FAIRVIEW HEIGHTS METROLINK STATION"
## [23] "BELLEVILLE METROLINK STATION"
## [24] "FAIRVIEW HEIGHTS LOOP @ METROLINK"
## [25] "SWANSEA LOOP @ METROLINK"
## [26] "GRAND METROLINK STATION"
## [27] "CENTRAL WEST END METROLINK STATION"
## [28] "FOREST PARK METROLINK STATION"
## [29] "DELMAR METROLINK STATION"
## [30] "FORSYTH @ FORSYTH METROLINK WB"
## [31] "PLYMOUTH @ WELLSTON METROLINK WB"
## [32] "BELLEVILLE LOOP @ METROLINK"
## [33] "COLLEGE METROLINK STATION"
## [34] "18TH STREET @ UNION STA METROLINK NB"
## [35] "DES PERES @ DELMAR LOOP METROLINK EB"
## [36] "RIVERVIEW-HALL METROBUS CENTER"
## [37] "SHREWSBURY METROBUS LOOP @ METROLINK"
## [38] "GRAND @ GRAND METROLINK NB"
## [39] "MAPLEWOOD MANCHESTER LOOP @ METROLINK"
## [40] "CLARK @ 18TH STREET METROLINK EB"
## [41] "RIVERVIEW-HALL METROBUS CENTER"
## [42] "WASHINGTON @ 6TH STREET METROLINK WB"
## [43] "CWE METROBUS CENTER @ METROLINK"
## [44] "RICHMOND HEIGHTS LOOP @ METROLINK"
## [45] "SKINKER METROLINK @ FOREST PARK PARKWAY SB"
## [46] "ROCK ROAD METROBUS LOOP @ METROLINK"
## [47] "MEMORIAL HOSPITAL METROLINK STATION"
## [48] "JJK CENTER METROLINK STATION"
## [49] "WASHINGTON PARK METROLINK STATION"
## [50] "COLLEGE LOOP @ METROLINK"
## [51] "SWANSEA METROLINK STATION"
## [52] "WASHINGTON PARK LOOP @ METROLINK"
## [53] "ROCK ROAD METROLINK STATION"
## [54] "CIVIC CENTER METROLINK STATION"
## [55] "JJK CENTER LOOP @ METROLINK"
## [56] "CLAYTON METROLINK STATION"
## [57] "FORSYTH METROLINK STATION"
## [58] "STADIUM METROLINK STATION"
## [59] "UMSL NORTH METROLINK STATION"
## [60] "18TH STREET @ CLARK METROLINK NB"
## [61] "DORSETT @ METRO WB"
## [62] "5TH & MISSOURI LOOP @ METROLINK"
## [63] "SHILOH SCOTT BASE LOOP @ METROLINK"
## [64] "MANCHESTER @ MAPLEWOOD METROLINK EB"
## [65] "SKINKER METROLINK STATION"
## [66] "U CITY BIG BEND METROLINK STATION"
## [67] "SHREWSBURY METROBUS LOOP @ METROLINK"
## [68] "WELLSTON METROLINK STATION"
## [69] "8TH AND PINE METROLINK STATION"
## [70] "UNION STA METROLINK STATION"
## [71] "MEMORIAL HOSPITAL LOOP @ METROLINK"
## [72] "ILLINOIS @ METROWAY SB"
## [73] "8TH STREET @ PINE METROLINK SB"
## [74] "GRAND @ GRAND METROLINK SB"
## [75] "EAST RIVERFRONT METROLINK STATION"
## [76] "5TH & MISSOURI METROLINK STATION"
## [77] "LACLEDES LANDING METROLINK STATION"
## [78] "LAMBERT MAIN TRML METROLINK STATION"
## [79] "LAMBERT EAST TRML METROLINK STATION"
## [80] "NORTH HANLEY METROLINK STATION"
## [81] "UMSL SOUTH METROLINK STATION"
## [82] "SHILOH-SCOTT LOOP @ METROLINK"
## [83] "18TH STREET @ UNION STA METROLINK SB"
## [84] "SHILOH-SCOTT METROLINK STATION"
## [85] "NORTH HANLEY METROBUS LOOP @ METROLINK"
## [86] "METRO MAIN SHOP GATE @ SPRUCE SB"
## [87] "CLAYTON METROBUS CENTER @ METROLINK"
## [88] "CWE METROBUS CENTER @ METROLINK"
## [89] "EMERSON PARK LOOP @ METROLINK"
## [90] "SPRUCE @ 14TH STREET METROLINK WB"
## [91] "PLYMOUTH @ WELLSTON METROLINK EB"stops[grep("METROLINK STATION", stops$stop_name), stop_name] # NO bus stops## [1] "CONVENTION CENTER METROLINK STATION"
## [2] "SUNNEN METROLINK STATION"
## [3] "MAPLEWOOD METROLINK STATION"
## [4] "BRENTWOOD METROLINK STATION"
## [5] "RICHMOND HEIGHTS METROLINK STATION"
## [6] "SHREWSBURY METROLINK STATION"
## [7] "EMERSON PARK METROLINK STATION"
## [8] "FAIRVIEW HEIGHTS METROLINK STATION"
## [9] "BELLEVILLE METROLINK STATION"
## [10] "GRAND METROLINK STATION"
## [11] "CENTRAL WEST END METROLINK STATION"
## [12] "FOREST PARK METROLINK STATION"
## [13] "DELMAR METROLINK STATION"
## [14] "COLLEGE METROLINK STATION"
## [15] "MEMORIAL HOSPITAL METROLINK STATION"
## [16] "JJK CENTER METROLINK STATION"
## [17] "WASHINGTON PARK METROLINK STATION"
## [18] "SWANSEA METROLINK STATION"
## [19] "ROCK ROAD METROLINK STATION"
## [20] "CIVIC CENTER METROLINK STATION"
## [21] "CLAYTON METROLINK STATION"
## [22] "FORSYTH METROLINK STATION"
## [23] "STADIUM METROLINK STATION"
## [24] "UMSL NORTH METROLINK STATION"
## [25] "SKINKER METROLINK STATION"
## [26] "WELLSTON METROLINK STATION"
## [27] "8TH AND PINE METROLINK STATION"
## [28] "UNION STA METROLINK STATION"
## [29] "EAST RIVERFRONT METROLINK STATION"
## [30] "5TH & MISSOURI METROLINK STATION"
## [31] "LACLEDES LANDING METROLINK STATION"
## [32] "LAMBERT MAIN TRML METROLINK STATION"
## [33] "LAMBERT EAST TRML METROLINK STATION"
## [34] "NORTH HANLEY METROLINK STATION"
## [35] "UMSL SOUTH METROLINK STATION"
## [36] "SHILOH-SCOTT METROLINK STATION"setdiff(stops[grep("METRO", stops$stop_name),stop_name],stops[grep("METROLINK STATION", stops$stop_name),stop_name])## [1] "METRO PLAZA @ SECTION LINE EB"
## [2] "METRO MAIN SHOP GATE @ SPRUCE NB"
## [3] "DES PERES @ DELMAR LOOP METROLINK WB"
## [4] "ROCK ROAD METROBUS LOOP @ METROLINK"
## [5] "WASHINGTON @ 6TH STREET METROLINK EB"
## [6] "CLAYTON METROBUS CENTER @ METROLINK"
## [7] "METRO PLAZA @ SECTION LINE WB"
## [8] "TAYLOR @ BROADWAY-TAYLOR METROBUS LOOP WB"
## [9] "DEBALIVIERE AVE @ FOREST PARK METROLINK NB"
## [10] "NORTH HANLEY METROBUS LOOP @ METROLINK"
## [11] "UMSL SOUTH METROBUS LOOP @ METROLINK"
## [12] "BRENTWOOD I-64 METROBUS LOOP @ METROLINK"
## [13] "DEBALIVIERE AVE @ FOREST PARK METROLINK SB"
## [14] "BALLAS METROBUS CENTER"
## [15] "FAIRVIEW HEIGHTS LOOP @ METROLINK"
## [16] "SWANSEA LOOP @ METROLINK"
## [17] "FORSYTH @ FORSYTH METROLINK WB"
## [18] "PLYMOUTH @ WELLSTON METROLINK WB"
## [19] "BELLEVILLE LOOP @ METROLINK"
## [20] "18TH STREET @ UNION STA METROLINK NB"
## [21] "DES PERES @ DELMAR LOOP METROLINK EB"
## [22] "RIVERVIEW-HALL METROBUS CENTER"
## [23] "SHREWSBURY METROBUS LOOP @ METROLINK"
## [24] "GRAND @ GRAND METROLINK NB"
## [25] "MAPLEWOOD MANCHESTER LOOP @ METROLINK"
## [26] "CLARK @ 18TH STREET METROLINK EB"
## [27] "WASHINGTON @ 6TH STREET METROLINK WB"
## [28] "CWE METROBUS CENTER @ METROLINK"
## [29] "RICHMOND HEIGHTS LOOP @ METROLINK"
## [30] "SKINKER METROLINK @ FOREST PARK PARKWAY SB"
## [31] "COLLEGE LOOP @ METROLINK"
## [32] "WASHINGTON PARK LOOP @ METROLINK"
## [33] "JJK CENTER LOOP @ METROLINK"
## [34] "18TH STREET @ CLARK METROLINK NB"
## [35] "DORSETT @ METRO WB"
## [36] "5TH & MISSOURI LOOP @ METROLINK"
## [37] "SHILOH SCOTT BASE LOOP @ METROLINK"
## [38] "MANCHESTER @ MAPLEWOOD METROLINK EB"
## [39] "U CITY BIG BEND METROLINK STATION"
## [40] "MEMORIAL HOSPITAL LOOP @ METROLINK"
## [41] "ILLINOIS @ METROWAY SB"
## [42] "8TH STREET @ PINE METROLINK SB"
## [43] "GRAND @ GRAND METROLINK SB"
## [44] "SHILOH-SCOTT LOOP @ METROLINK"
## [45] "18TH STREET @ UNION STA METROLINK SB"
## [46] "METRO MAIN SHOP GATE @ SPRUCE SB"
## [47] "EMERSON PARK LOOP @ METROLINK"
## [48] "SPRUCE @ 14TH STREET METROLINK WB"
## [49] "PLYMOUTH @ WELLSTON METROLINK EB"(stops_metro <- stops[grep("METROLINK STATION", stops$stop_name)])## stop_lat zone_id stop_lon stop_id stop_desc
## 1: 38.63022 NA -90.18940 10619
## 2: 38.60717 NA -90.33029 14754
## 3: 38.61367 NA -90.33113 14755
## 4: 38.62834 NA -90.33811 14756
## 5: 38.63514 NA -90.34234 14757
## 6: 38.59287 NA -90.31944 14753
## 7: 38.62911 NA -90.13723 10599
## 8: 38.59381 NA -90.04794 10602
## 9: 38.52183 NA -89.97436 10605
## 10: 38.62951 NA -90.23546 10624
## 11: 38.63583 NA -90.26194 10625
## 12: 38.64778 NA -90.28465 10626
## 13: 38.65572 NA -90.29459 10627
## 14: 38.51618 NA -89.91967 11103
## 15: 38.55757 NA -90.01536 10603
## 16: 38.62324 NA -90.12441 10600
## 17: 38.61374 NA -90.09529 10601
## 18: 38.53627 NA -89.98748 10604
## 19: 38.68524 NA -90.30143 10629
## 20: 38.62501 NA -90.20307 10622
## 21: 38.64565 NA -90.33926 14758
## 22: 38.64893 NA -90.32814 14759
## 23: 38.62341 NA -90.19454 13662
## 24: 38.71285 NA -90.30612 10631
## 25: 38.64936 NA -90.30081 14761
## 26: 38.66889 NA -90.29841 10628
## 27: 38.62791 NA -90.19271 10620
## 28: 38.62659 NA -90.20655 10623
## 29: 38.62851 NA -90.17475 10617
## 30: 38.62506 NA -90.15938 10616
## 31: 38.62946 NA -90.18364 10618
## 32: 38.74122 NA -90.36460 10634
## 33: 38.73617 NA -90.35603 10633
## 34: 38.71996 NA -90.31521 10632
## 35: 38.70526 NA -90.30514 10630
## 36: 38.53909 NA -89.87917 14274
## stop_lat zone_id stop_lon stop_id stop_desc
## stop_name location_type stop_code
## 1: CONVENTION CENTER METROLINK STATION 0 10619
## 2: SUNNEN METROLINK STATION 0 14754
## 3: MAPLEWOOD METROLINK STATION 0 14755
## 4: BRENTWOOD METROLINK STATION 0 14756
## 5: RICHMOND HEIGHTS METROLINK STATION 0 14757
## 6: SHREWSBURY METROLINK STATION 0 14753
## 7: EMERSON PARK METROLINK STATION 0 10599
## 8: FAIRVIEW HEIGHTS METROLINK STATION 0 10602
## 9: BELLEVILLE METROLINK STATION 0 10605
## 10: GRAND METROLINK STATION 0 10624
## 11: CENTRAL WEST END METROLINK STATION 0 10625
## 12: FOREST PARK METROLINK STATION 0 10626
## 13: DELMAR METROLINK STATION 0 10627
## 14: COLLEGE METROLINK STATION 0 11103
## 15: MEMORIAL HOSPITAL METROLINK STATION 0 10603
## 16: JJK CENTER METROLINK STATION 0 10600
## 17: WASHINGTON PARK METROLINK STATION 0 10601
## 18: SWANSEA METROLINK STATION 0 10604
## 19: ROCK ROAD METROLINK STATION 0 10629
## 20: CIVIC CENTER METROLINK STATION 0 10622
## 21: CLAYTON METROLINK STATION 0 14758
## 22: FORSYTH METROLINK STATION 0 14759
## 23: STADIUM METROLINK STATION 0 13662
## 24: UMSL NORTH METROLINK STATION 0 10631
## 25: SKINKER METROLINK STATION 0 14761
## 26: WELLSTON METROLINK STATION 0 10628
## 27: 8TH AND PINE METROLINK STATION 0 10620
## 28: UNION STA METROLINK STATION 0 10623
## 29: EAST RIVERFRONT METROLINK STATION 0 10617
## 30: 5TH & MISSOURI METROLINK STATION 0 10616
## 31: LACLEDES LANDING METROLINK STATION 0 10618
## 32: LAMBERT MAIN TRML METROLINK STATION 0 10634
## 33: LAMBERT EAST TRML METROLINK STATION 0 10633
## 34: NORTH HANLEY METROLINK STATION 0 10632
## 35: UMSL SOUTH METROLINK STATION 0 10630
## 36: SHILOH-SCOTT METROLINK STATION 0 14274
## stop_name location_type stop_codestops_notmetro <- setdiff(stops, stops_metro)
routes_metro <- routes[route_type==2]
shapes_metro <- shapes %>%
filter(shape_id %in% trips$shape_id[trips$route_id %in% routes_metro$route_id]) %>%
arrange(shape_id, shape_pt_sequence)
### or, if you prefer data.table subsetting
shapes_metro.dt <- shapes[shape_id%in% trips[route_id%in% routes_metro$route_id, shape_id]]
setkey(shapes_metro.dt, shape_id, shape_pt_sequence)
shapes_colors <- left_join(left_join(shapes %>% select(shape_id) %>% unique(),
trips %>% select(shape_id, route_id) %>% unique(),
by = "shape_id"),
routes %>% select(route_id, route_color) %>% unique(),
by = "route_id") %>%
mutate(route_color = paste0("#", route_color))
shapes_colors_metro <- shapes_colors %>%
filter(shape_id %in% trips$shape_id[trips$route_id %in% routes_metro$route_id]) %>% unique() %>% arrange(shape_id)First map of Metrolink route
p2 <- ggplot() + geom_path(data = shapes_metro.dt,
aes(shape_pt_lon, shape_pt_lat, group = shape_id, color = factor(shape_id)),
size = 2, alpha = .7) +
scale_color_manual(values = shapes_colors_metro$route_color) +
geom_point(data = stops_metro,
aes(stop_lon, stop_lat), shape = 21, colour = "white", alpha = 1) +
coord_equal() + theme_map() +
theme(plot.background = element_rect(fill = "black", colour = "black"),
title = element_text(hjust = 1, colour = "white", size = 8),
legend.position = "none") +
ggtitle("St. Louis Metro's public transport system")
p2
Difference in the routes?
Faceted map
miniMetros <- ggplot() + geom_path(data = shapes_metro.dt,
aes(shape_pt_lon, shape_pt_lat, group = shape_id, color = factor(shape_id)),
size = 2, alpha = .7) +
scale_color_manual(values = shapes_colors_metro$route_color) +
geom_point(data = stops_metro,
aes(stop_lon, stop_lat), shape = 21, colour = "white", alpha = .5) +
coord_equal() + theme_map() + facet_wrap(~shape_id,ncol=6) +
theme(plot.background = element_rect(fill = "black", colour = "black"),
title = element_text(hjust = 1, colour = "white", size = 8),
legend.position = "none") +
ggtitle("The shapes of St. Louis Metro's public transport system")
miniMetros
Map with bus stops
p3 <- p2 +geom_point(data = stops_notmetro, aes(stop_lon, stop_lat),
shape = 21, colour = "green", alpha = .2)
p3
Impressive, but the Metrolink is lost under the bus stops. Reorder code to put metro layer back on top.
p4<- p3 + geom_path(data = shapes_metro,
aes(shape_pt_lon, shape_pt_lat, group = shape_id,
color = factor(shape_id)),
size = 2, alpha = .8) +
geom_point(data = stops_metro,
aes(stop_lon, stop_lat),
shape = 21, colour = "white", alpha = .8, size = 3)
p4
# Just Metro route
p5 <- ggplot() +
geom_path(data = shapes_metro,
aes(shape_pt_lon, shape_pt_lat, group = shape_id, colour = factor(shape_id)),
size = 2, alpha = .8) +
scale_color_manual(values = shapes_colors_metro$route_color) +
geom_point(data = stops_metro,
aes(stop_lon, stop_lat),
shape = 21, colour = "white", alpha = .8, size = 3) +
coord_equal() +
theme_map() +
theme(plot.background = element_rect(fill = "black", color = "black"),
title = element_text(hjust = 1, color = "white", size = 8),
legend.position = "none")
p5 + ggtitle("St. Louis METRO")
## Insert basemap behind Metro routes
Here we reach for Leaflet, a javascript-based library that allows for interactive maps. You can read about their R package at https://rstudio.github.io/leaflet/ or peruse the full javascript library documentation at http://leafletjs.com.
library(leaflet)
m <- leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addMarkers(lng=-90.2489464, lat=38.6362105, popup="STL R User's Group Meetup at CIC@CET")
m # Print the mapjust91322<-shapes_metro[which(shapes_metro$shape_id==91322), c("shape_pt_lon", "shape_pt_lat")]
leaflet(as.matrix(just91322), options = leafletOptions(minZoom = 9, maxZoom = 18)) %>% addTiles() %>%
addPolylines(color = "red", weight = 4, smoothFactor = 0.5,
opacity = 1.0, highlightOptions = highlightOptions(color = "pink", weight = 4,
bringToFront = TRUE))just91318<-shapes_metro[which(shapes_metro$shape_id==91318), c("shape_pt_lon", "shape_pt_lat")]
allMetrolink <-rbind(just91322, c(NA, NA), just91318)
leaflet(as.matrix(allMetrolink), options = leafletOptions(minZoom = 9, maxZoom = 18)) %>%
addTiles() %>% addPolylines(color = c("red", "blue"), weight = 4, smoothFactor = 0.5,
opacity = 1.0, highlightOptions = highlightOptions(color = "yellow", weight = 4,
bringToFront = TRUE)) Use different tiles for change in basemap
A list of choices of basemap styles, together with preview images is provided at http://leaflet-extras.github.io/leaflet-providers/preview/index.html.
names(providers)## [1] "OpenStreetMap"
## [2] "OpenStreetMap.Mapnik"
## [3] "OpenStreetMap.BlackAndWhite"
## [4] "OpenStreetMap.DE"
## [5] "OpenStreetMap.France"
## [6] "OpenStreetMap.HOT"
## [7] "OpenSeaMap"
## [8] "OpenTopoMap"
## [9] "Thunderforest"
## [10] "Thunderforest.OpenCycleMap"
## [11] "Thunderforest.Transport"
## [12] "Thunderforest.TransportDark"
## [13] "Thunderforest.SpinalMap"
## [14] "Thunderforest.Landscape"
## [15] "Thunderforest.Outdoors"
## [16] "Thunderforest.Pioneer"
## [17] "OpenMapSurfer"
## [18] "OpenMapSurfer.Roads"
## [19] "OpenMapSurfer.AdminBounds"
## [20] "OpenMapSurfer.Grayscale"
## [21] "Hydda"
## [22] "Hydda.Full"
## [23] "Hydda.Base"
## [24] "Hydda.RoadsAndLabels"
## [25] "MapBox"
## [26] "Stamen"
## [27] "Stamen.Toner"
## [28] "Stamen.TonerBackground"
## [29] "Stamen.TonerHybrid"
## [30] "Stamen.TonerLines"
## [31] "Stamen.TonerLabels"
## [32] "Stamen.TonerLite"
## [33] "Stamen.Watercolor"
## [34] "Stamen.Terrain"
## [35] "Stamen.TerrainBackground"
## [36] "Stamen.TopOSMRelief"
## [37] "Stamen.TopOSMFeatures"
## [38] "Esri"
## [39] "Esri.WorldStreetMap"
## [40] "Esri.DeLorme"
## [41] "Esri.WorldTopoMap"
## [42] "Esri.WorldImagery"
## [43] "Esri.WorldTerrain"
## [44] "Esri.WorldShadedRelief"
## [45] "Esri.WorldPhysical"
## [46] "Esri.OceanBasemap"
## [47] "Esri.NatGeoWorldMap"
## [48] "Esri.WorldGrayCanvas"
## [49] "OpenWeatherMap"
## [50] "OpenWeatherMap.Clouds"
## [51] "OpenWeatherMap.CloudsClassic"
## [52] "OpenWeatherMap.Precipitation"
## [53] "OpenWeatherMap.PrecipitationClassic"
## [54] "OpenWeatherMap.Rain"
## [55] "OpenWeatherMap.RainClassic"
## [56] "OpenWeatherMap.Pressure"
## [57] "OpenWeatherMap.PressureContour"
## [58] "OpenWeatherMap.Wind"
## [59] "OpenWeatherMap.Temperature"
## [60] "OpenWeatherMap.Snow"
## [61] "HERE"
## [62] "HERE.normalDay"
## [63] "HERE.normalDayCustom"
## [64] "HERE.normalDayGrey"
## [65] "HERE.normalDayMobile"
## [66] "HERE.normalDayGreyMobile"
## [67] "HERE.normalDayTransit"
## [68] "HERE.normalDayTransitMobile"
## [69] "HERE.normalNight"
## [70] "HERE.normalNightMobile"
## [71] "HERE.normalNightGrey"
## [72] "HERE.normalNightGreyMobile"
## [73] "HERE.basicMap"
## [74] "HERE.mapLabels"
## [75] "HERE.trafficFlow"
## [76] "HERE.carnavDayGrey"
## [77] "HERE.hybridDay"
## [78] "HERE.hybridDayMobile"
## [79] "HERE.pedestrianDay"
## [80] "HERE.pedestrianNight"
## [81] "HERE.satelliteDay"
## [82] "HERE.terrainDay"
## [83] "HERE.terrainDayMobile"
## [84] "FreeMapSK"
## [85] "MtbMap"
## [86] "CartoDB"
## [87] "CartoDB.Positron"
## [88] "CartoDB.PositronNoLabels"
## [89] "CartoDB.PositronOnlyLabels"
## [90] "CartoDB.DarkMatter"
## [91] "CartoDB.DarkMatterNoLabels"
## [92] "CartoDB.DarkMatterOnlyLabels"
## [93] "HikeBike"
## [94] "HikeBike.HikeBike"
## [95] "HikeBike.HillShading"
## [96] "BasemapAT"
## [97] "BasemapAT.basemap"
## [98] "BasemapAT.grau"
## [99] "BasemapAT.overlay"
## [100] "BasemapAT.highdpi"
## [101] "BasemapAT.orthofoto"
## [102] "NASAGIBS"
## [103] "NASAGIBS.ModisTerraTrueColorCR"
## [104] "NASAGIBS.ModisTerraBands367CR"
## [105] "NASAGIBS.ViirsEarthAtNight2012"
## [106] "NASAGIBS.ModisTerraLSTDay"
## [107] "NASAGIBS.ModisTerraSnowCover"
## [108] "NASAGIBS.ModisTerraAOD"
## [109] "NASAGIBS.ModisTerraChlorophyll"
## [110] "NLS"leaflet(as.matrix(allMetrolink),
options = leafletOptions(minZoom = 10, maxZoom = 18)) %>%
addProviderTiles("CartoDB.DarkMatter") %>%
addPolylines(color = c("red", "blue"), weight = 4,
smoothFactor = 0.5, opacity = 1.0,
highlightOptions = highlightOptions(color = "yellow",
weight = 4,
bringToFront = TRUE)) %>%
addLegend(title = "St. Louis MetroLink Light Rail System",
colors = c("red", "blue"),
labels = c("MetroLink Red Line","MetroLink Blue Line"), opacity = 1)Mapping St. Louis County
In this example, we show how to work with shapefile data within R. We obtain a shapefile of municipal boundaries from http://data.stlouisco.com/. (In particular, we are using this file: <>.)
library("rgdal") #load R interface to the Geospatial Abstraction Library (GDAL)## Loading required package: sp## rgdal: version: 1.2-5, (SVN revision 648)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.0.1, released 2015/09/15
## Path to GDAL shared files: C:/Users/cindytraub/Documents/R/win-library/3.3/rgdal/gdal
## Loaded PROJ.4 runtime: Rel. 4.9.2, 08 September 2015, [PJ_VERSION: 492]
## Path to PROJ.4 shared files: C:/Users/cindytraub/Documents/R/win-library/3.3/rgdal/proj
## Linking to sp version: 1.2-4library("rgeos")## rgeos version: 0.3-22, (SVN revision 544)
## GEOS runtime version: 3.5.0-CAPI-1.9.0 r4084
## Linking to sp version: 1.2-4
## Polygon checking: TRUE### NOTE: ###
## Shapefile path is sensitive to trailing slash - needed on Mac, not on PC
# myShapefilePath<- "/Users/CTatWUSTL/Box Sync/CT_presentations/MeetupSpatialR/shapefiles/Municipal_Boundaries/"
myShapefilePath<- "C:/Users/cindytraub/Box Sync/CT_presentations/MeetupSpatialR/shapefiles/Municipal_Boundaries"
setwd(myShapefilePath)
dir() # should see MunicipalBoundaries (5 files)## [1] "Municipal_Boundaries.cpg" "Municipal_Boundaries.dbf"
## [3] "Municipal_Boundaries.prj" "Municipal_Boundaries.shp"
## [5] "Municipal_Boundaries.shx"stlco<-readOGR(dsn = myShapefilePath,layer = "Municipal_Boundaries") #read in shapefile## OGR data source with driver: ESRI Shapefile
## Source: "C:/Users/cindytraub/Box Sync/CT_presentations/MeetupSpatialR/shapefiles/Municipal_Boundaries", layer: "Municipal_Boundaries"
## with 126 features
## It has 7 fields
## Integer64 fields read as strings: OBJECTID### dsn is directory where data is stored
### layer specifies the file name
### no need for file extension like .shpExamining what types of data are in the shapefile
The traditional “shape of your data” commands give some inkling to what things look like. One difference from working with simple data frames is the use of the “@” sign to access different slots in the SpatialPolygonsDataFrame. The "@data" slot stores a data frame associated with attribute data.
summary(stlco)## Object of class SpatialPolygonsDataFrame
## Coordinates:
## min max
## x -90.73621 -90.11769
## y 38.38781 38.89175
## Is projected: FALSE
## proj4string :
## [+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0]
## Data attributes:
## OBJECTID MUNICIPALI MUNI MUNICODE
## 1 : 1 UNINCORPORATED :36 UNI :36 000 :36
## 10 : 1 BALLWIN : 1 BAL : 1 002 : 1
## 100 : 1 BEL-NOR : 1 BEV : 1 003 : 1
## 101 : 1 BEL-RIDGE : 1 BFN : 1 004 : 1
## 102 : 1 BELLA VILLA : 1 BKJ : 1 005 : 1
## 103 : 1 BELLEFONTAINE NEIGHBORS: 1 BLN : 1 007 : 1
## (Other):120 (Other) :85 (Other):85 (Other):85
## SQ_MILES last_edite
## Min. : 0.00026 2015-08-06T01:27:52.000Z:62
## 1st Qu.: 0.11979 2015-08-06T01:27:51.000Z:32
## Median : 0.62695 2015-11-10T14:18:34.000Z: 1
## Mean : 4.15746 2015-11-10T14:18:52.000Z: 1
## 3rd Qu.: 2.86980 2015-11-18T17:58:47.000Z: 1
## Max. :93.11465 2015-12-15T15:05:30.000Z: 1
## (Other) :28
## GlobalID
## {044E6A3D-9FE8-4A71-9ADA-B4DDFDC6D0E5}: 1
## {048E191C-37F2-499E-9959-F55987F1E60B}: 1
## {04C057E6-BB0F-4A6D-B251-F9BCE363EE77}: 1
## {0C8F2EED-43D3-4E1C-8451-01EB32CF36A4}: 1
## {0CD7A2FA-44D9-4DDE-BB35-9976DB4B3E1C}: 1
## {0D8F1E11-9EAD-4335-B13E-F3B6ACD8ED0F}: 1
## (Other) :120dim(stlco)## [1] 126 7summary(stlco@data)## OBJECTID MUNICIPALI MUNI MUNICODE
## 1 : 1 UNINCORPORATED :36 UNI :36 000 :36
## 10 : 1 BALLWIN : 1 BAL : 1 002 : 1
## 100 : 1 BEL-NOR : 1 BEV : 1 003 : 1
## 101 : 1 BEL-RIDGE : 1 BFN : 1 004 : 1
## 102 : 1 BELLA VILLA : 1 BKJ : 1 005 : 1
## 103 : 1 BELLEFONTAINE NEIGHBORS: 1 BLN : 1 007 : 1
## (Other):120 (Other) :85 (Other):85 (Other):85
## SQ_MILES last_edite
## Min. : 0.00026 2015-08-06T01:27:52.000Z:62
## 1st Qu.: 0.11979 2015-08-06T01:27:51.000Z:32
## Median : 0.62695 2015-11-10T14:18:34.000Z: 1
## Mean : 4.15746 2015-11-10T14:18:52.000Z: 1
## 3rd Qu.: 2.86980 2015-11-18T17:58:47.000Z: 1
## Max. :93.11465 2015-12-15T15:05:30.000Z: 1
## (Other) :28
## GlobalID
## {044E6A3D-9FE8-4A71-9ADA-B4DDFDC6D0E5}: 1
## {048E191C-37F2-499E-9959-F55987F1E60B}: 1
## {04C057E6-BB0F-4A6D-B251-F9BCE363EE77}: 1
## {0C8F2EED-43D3-4E1C-8451-01EB32CF36A4}: 1
## {0CD7A2FA-44D9-4DDE-BB35-9976DB4B3E1C}: 1
## {0D8F1E11-9EAD-4335-B13E-F3B6ACD8ED0F}: 1
## (Other) :120stlco@data$MUNI## [1] BRK UNI CAL HAZ FEN EUR UNI MAN UNI UNI BLR UNI CLY STJ UNI KIN GLN
## [18] EDM UNI OAK SUN BLN WEB NWC NOR MOL BRH WAR LAD WOD PAC UNI CMP RIV
## [35] UNI DEL BFN UNI BKJ GRN UNI WIL UNI MAC WIN RKH UNI MPL CLP HUN CLA
## [52] BRW RMH FRT OLV UNI VIT WEL HLD HLH UPL VEH VIP BEV PIN UNI UNI VEL
## [69] UNI UNI UNI UNI BRV UNI UNI UNI UNI GPK LAK BLV GWV TWO UNI UNI UNI
## [86] GEP UNI UNI NTH UNI PAH PAP UNI UNI UCT PGD CCH CHF SYC CHA OVR COV
## [103] FER FLH JEN KRW MRB SHR CLK CRE STA UNI DES UNI TAC WES CCR MRH BRG
## [120] ELV BAL WWD FLO UNI VAL UNI
## 91 Levels: BAL BEV BFN BKJ BLN BLR BLV BRG BRH BRK BRV BRW CAL CCH ... WWDhead(stlco@polygons[[1]]@Polygons[[1]]@coords) ### look at first few coordinates of 1st polygon## [,1] [,2]
## [1,] -90.32494 38.76475
## [2,] -90.32499 38.76437
## [3,] -90.32500 38.76431
## [4,] -90.32500 38.76430
## [5,] -90.32502 38.76420
## [6,] -90.32505 38.76403plot(stlco@polygons[[1]]@Polygons[[1]]@coords) ### graph coords of 1st polygon
head(stlco@data, n=10) ### look at the first 10 rows of data## OBJECTID MUNICIPALI MUNI MUNICODE SQ_MILES
## 0 1 BERKELEY BRK 007 4.96244058
## 1 2 UNINCORPORATED UNI 000 2.14454346
## 2 3 CALVERTON PARK CAL 012 0.41551487
## 3 4 HAZELWOOD HAZ 080 16.58132350
## 4 5 FENTON FEN 021 6.39126540
## 5 6 EUREKA EUR 098 10.61113783
## 6 7 UNINCORPORATED UNI 000 0.01190030
## 7 8 MANCHESTER MAN 094 5.05194587
## 8 9 UNINCORPORATED UNI 000 0.00257117
## 9 10 UNINCORPORATED UNI 000 0.29064989
## last_edite GlobalID
## 0 2015-08-06T01:27:52.000Z {39136D96-064C-4835-A88E-CEC13721AA7D}
## 1 2015-08-06T01:27:52.000Z {7DF0D7ED-3E1C-43B0-9F68-76A48D74F1CA}
## 2 2015-08-06T01:27:52.000Z {736A980F-C155-455D-96B9-7FD9B4DF615E}
## 3 2015-08-06T01:27:52.000Z {B69A901C-29C0-46AC-BB3A-162BF197D790}
## 4 2015-08-06T01:27:52.000Z {BFCBF99C-FBB3-4020-B026-37788F9A3C51}
## 5 2015-08-06T01:27:52.000Z {0E1952C6-DD29-4E5E-A34F-A7F7F2DFE042}
## 6 2015-08-06T01:27:52.000Z {7E719A45-B726-49AF-87EA-2298C6650551}
## 7 2015-08-06T01:27:52.000Z {0C8F2EED-43D3-4E1C-8451-01EB32CF36A4}
## 8 2015-08-06T01:27:52.000Z {EEAD0830-27AB-4E1E-80FD-69CA61B7142C}
## 9 2015-08-06T01:27:52.000Z {411FAAAC-D959-4FB9-A7A9-BBA680392963}mean(stlco@data$SQ_MILES) ### compute the average area## [1] 4.15746names(stlco@data)## [1] "OBJECTID" "MUNICIPALI" "MUNI" "MUNICODE" "SQ_MILES"
## [6] "last_edite" "GlobalID"plot(stlco) ### plots the map of St. Louis County Municipal boundaries
# Is this projected?
stlco@proj4string## CRS arguments:
## +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0proj4string(stlco)## [1] "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"# If YES, you may wish to store the projection for later operations.
stlcoCRS<-proj4string(stlco)A little exploratory data analysis
# Named Municipalities
stlco@data[!stlco@data$MUNI=="UNI",]## OBJECTID MUNICIPALI MUNI MUNICODE SQ_MILES
## 0 1 BERKELEY BRK 007 4.96244058
## 2 3 CALVERTON PARK CAL 012 0.41551487
## 3 4 HAZELWOOD HAZ 080 16.58132350
## 4 5 FENTON FEN 021 6.39126540
## 5 6 EUREKA EUR 098 10.61113783
## 7 8 MANCHESTER MAN 094 5.05194587
## 10 11 BEL-RIDGE BLR 005 0.77326350
## 12 13 CLAYTON CLY 014 2.50957127
## 13 14 ST. JOHN STJ 051 1.42858470
## 15 16 KINLOCH KIN 074 0.73391852
## 16 17 GLENDALE GLN 026 1.30133559
## 17 18 EDMUNDSON EDM 073 0.27426663
## 19 20 OAKLAND OAK 041 0.59968812
## 20 21 SUNSET HILLS SUN 101 9.04584206
## 21 22 BEL-NOR BLN 004 0.62799875
## 22 23 WEBSTER GROVES WEB 064 5.90369884
## 23 24 NORWOOD COURT NWC 077 0.12322321
## 24 25 NORMANDY NOR 039 1.84493864
## 25 26 MOLINE ACRES MOL 081 0.57058670
## 26 27 BRECKENRIDGE HILLS BRH 084 0.79829633
## 27 28 WARSON WOODS WAR 063 0.56398310
## 28 29 LADUE LAD 033 8.54510700
## 29 30 WOODSON TERRACE WOD 067 0.77796253
## 30 31 PACIFIC PAC 044 0.60664742
## 32 33 CHAMP CMP 102 0.79849851
## 33 34 RIVERVIEW RIV 089 0.83033680
## 35 36 DELLWOOD DEL 095 1.03867972
## 36 37 BELLEFONTAINE NEIGHBORS BFN 088 4.35441159
## 38 39 BLACK JACK BKJ 103 2.61431547
## 39 40 GREENDALE GRN 086 0.18683104
## 41 42 WILBUR PARK WIL 065 0.05927348
## 43 44 MACKENZIE MAC 034 0.02000011
## 44 45 WINCHESTER WIN 066 0.25038523
## 45 46 ROCK HILL RKH 050 1.09568024
## 47 48 MAPLEWOOD MPL 035 1.56402614
## 48 49 CRYSTAL LAKE PARK CLP 016 0.09487193
## 49 50 HUNTLEIGH HUN 030 0.98690032
## 50 51 COUNTRY LIFE ACRES CLA 079 0.12232057
## 51 52 BRENTWOOD BRW 009 1.94662510
## 52 53 RICHMOND HEIGHTS RMH 049 2.28854519
## 53 54 FRONTENAC FRT 025 2.90266239
## 54 55 OLIVETTE OLV 042 2.77122775
## 56 57 VINITA TERRACE VIT 062 0.06364946
## 57 58 WELLSTON WEL 078 0.92742713
## 58 59 HILLSDALE HLD 068 0.34367881
## 59 60 HANLEY HILLS HLH 076 0.34878777
## 60 61 UPLANDS PARK UPL 056 0.06855475
## 61 62 VELDA VILLAGE HILLS VEH 060 0.11961283
## 62 63 VINITA PARK VIP 061 0.72404647
## 63 64 BEVERLY HILLS BEV 008 0.09532134
## 64 65 PINE LAWN PIN 048 0.61458900
## 67 68 VELDA CITY VEL 059 0.16755216
## 72 73 BELLERIVE ACRES BRV 003 0.34104089
## 77 78 GREEN PARK GPK 106 1.30852088
## 78 79 LAKESHIRE LAK 070 0.21771170
## 79 80 BELLA VILLA BLV 002 0.12646229
## 80 81 GRANTWOOD VILLAGE GWV 029 0.80898748
## 81 82 TWIN OAKS TWO 055 0.27109417
## 85 86 GLEN ECHO PARK GEP 027 0.03122508
## 88 89 NORTHWOODS NTH 040 0.67333775
## 90 91 PASADENA HILLS PAH 045 0.21138557
## 91 92 PASADENA PARK PAP 046 0.09142287
## 94 95 UNIVERSITY CITY UCT 057 5.89802311
## 95 96 PAGEDALE PGD 085 1.21159061
## 96 97 COUNTRY CLUB HILLS CCH 015 0.18662630
## 97 98 CHESTERFIELD CHF 105 33.63169434
## 98 99 SYCAMORE HILLS SYC 054 0.13158481
## 99 100 CHARLACK CHA 013 0.26761734
## 100 101 OVERLAND OVR 043 4.39103293
## 101 102 COOL VALLEY COV 096 0.45755712
## 102 103 FERGUSON FER 022 6.15977656
## 103 104 FLORDELL HILLS FLH 023 0.12259412
## 104 105 JENNINGS JEN 031 3.74531144
## 105 106 KIRKWOOD KRW 032 9.17943048
## 106 107 MARLBOROUGH MRB 037 0.23570180
## 107 108 SHREWSBURY SHR 053 1.43406078
## 108 109 CLARKSON VALLEY CLK 087 2.72882982
## 109 110 CRESTWOOD CRE 069 3.57763326
## 110 111 ST. ANN STA 072 3.15564650
## 112 113 DES PERES DES 017 4.33635907
## 114 115 TOWN & COUNTRY TAC 083 11.54651725
## 115 116 WESTWOOD WES 097 0.62589883
## 116 117 CREVE COEUR CCR 082 10.27503344
## 117 118 MARYLAND HEIGHTS MRH 104 23.53182062
## 118 119 BRIDGETON BRG 010 15.15525216
## 119 120 ELLISVILLE ELV 019 4.37323406
## 120 121 BALLWIN BAL 093 8.94001252
## 121 122 WILDWOOD WWD 107 67.20598254
## 122 123 FLORISSANT FLO 024 12.80797750
## 124 125 VALLEY PARK VAL 058 4.24373456
## last_edite GlobalID
## 0 2015-08-06T01:27:52.000Z {39136D96-064C-4835-A88E-CEC13721AA7D}
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## 100 2015-12-30T15:23:13.000Z {5B59B7DD-E0A0-45F3-B7CE-0E64A7259444}
## 101 2015-12-30T19:08:34.000Z {F6547AB0-7D88-4A3A-8BB1-B600CA768DAF}
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## 103 2016-03-29T13:45:35.000Z {AE748ADC-5D6F-4F2D-98A3-C2A1639EC210}
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## 109 2016-06-02T12:42:30.000Z {88AC3195-A779-4EDB-AE9D-A2C289E94896}
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## 119 2016-09-28T17:04:18.000Z {365F8A9D-EDE6-4F6A-B354-416757CDA1A0}
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## 122 2017-03-02T19:29:25.000Z {C1B23B95-CFDE-4AE4-ADAB-1C2C307233F1}
## 124 2017-03-06T17:59:51.000Z {6BFD68C8-CDDF-4698-8240-E15D330FC172}mean(stlco@data[!stlco@data$MUNI=="UNI",]$SQ_MILES)## [1] 3.91199summary(stlco@data[!stlco@data$MUNI=="UNI",]$SQ_MILES)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0200 0.2719 0.8789 3.9120 4.3130 67.2100table(stlco@data[!stlco@data$MUNI=="UNI",]$SQ_MILES)##
## 0.02000011 0.03122508 0.05927348 0.06364946 0.06855475 0.09142287
## 1 1 1 1 1 1
## 0.09487193 0.09532134 0.11961283 0.12232057 0.12259412 0.12322321
## 1 1 1 1 1 1
## 0.12646229 0.13158481 0.16755216 0.1866263 0.18683104 0.21138557
## 1 1 1 1 1 1
## 0.2177117 0.2357018 0.25038523 0.26761734 0.27109417 0.27426663
## 1 1 1 1 1 1
## 0.34104089 0.34367881 0.34878777 0.41551487 0.45755712 0.5639831
## 1 1 1 1 1 1
## 0.5705867 0.59968812 0.60664742 0.614589 0.62589883 0.62799875
## 1 1 1 1 1 1
## 0.67333775 0.72404647 0.73391852 0.7732635 0.77796253 0.79829633
## 1 1 1 1 1 1
## 0.79849851 0.80898748 0.8303368 0.92742713 0.98690032 1.03867972
## 1 1 1 1 1 1
## 1.09568024 1.21159061 1.30133559 1.30852088 1.4285847 1.43406078
## 1 1 1 1 1 1
## 1.56402614 1.84493864 1.9466251 2.28854519 2.50957127 2.61431547
## 1 1 1 1 1 1
## 2.72882982 2.77122775 2.90266239 3.1556465 3.57763326 3.74531144
## 1 1 1 1 1 1
## 4.24373456 4.33635907 4.35441159 4.37323406 4.39103293 4.96244058
## 1 1 1 1 1 1
## 5.05194587 5.89802311 5.90369884 6.15977656 6.3912654 8.545107
## 1 1 1 1 1 1
## 8.94001252 9.04584206 9.17943048 10.27503344 10.61113783 11.54651725
## 1 1 1 1 1 1
## 12.8079775 15.15525216 16.5813235 23.53182062 33.63169434 67.20598254
## 1 1 1 1 1 1stlco@data[!stlco@data$MUNI=="UNI"&stlco@data$SQ_MILES>=50,]## OBJECTID MUNICIPALI MUNI MUNICODE SQ_MILES last_edite
## 121 122 WILDWOOD WWD 107 67.20598 2016-10-26T14:20:29.000Z
## GlobalID
## 121 {B6F58D70-F575-42E8-B5E2-765E3D083C0F}mean(stlco@data[!stlco@data$MUNI=="UNI"&stlco@data$SQ_MILES<50,]$SQ_MILES)## [1] 3.200821#Unincorporated
stlco@data[stlco@data$MUNI=="UNI",]## OBJECTID MUNICIPALI MUNI MUNICODE SQ_MILES
## 1 2 UNINCORPORATED UNI 000 2.14454346
## 6 7 UNINCORPORATED UNI 000 0.01190030
## 8 9 UNINCORPORATED UNI 000 0.00257117
## 9 10 UNINCORPORATED UNI 000 0.29064989
## 11 12 UNINCORPORATED UNI 000 0.16942913
## 14 15 UNINCORPORATED UNI 000 0.00048256
## 18 19 UNINCORPORATED UNI 000 0.01942226
## 31 32 UNINCORPORATED UNI 000 7.57627964
## 34 35 UNINCORPORATED UNI 000 0.00232265
## 37 38 UNINCORPORATED UNI 000 2.35408766
## 40 41 UNINCORPORATED UNI 000 1.64609768
## 42 43 UNINCORPORATED UNI 000 0.02690897
## 46 47 UNINCORPORATED UNI 000 0.00589622
## 55 56 UNINCORPORATED UNI 000 0.12030614
## 65 66 UNINCORPORATED UNI 000 0.92742970
## 66 67 UNINCORPORATED UNI 000 0.15428515
## 68 69 UNINCORPORATED UNI 000 0.00026321
## 69 70 UNINCORPORATED UNI 000 0.00047754
## 70 71 UNINCORPORATED UNI 000 0.01007022
## 71 72 UNINCORPORATED UNI 000 0.00087053
## 73 74 UNINCORPORATED UNI 000 0.00638866
## 74 75 UNINCORPORATED UNI 000 0.00400754
## 75 76 UNINCORPORATED UNI 000 0.00663480
## 76 77 UNINCORPORATED UNI 000 0.00135399
## 82 83 UNINCORPORATED UNI 000 0.00173643
## 83 84 UNINCORPORATED UNI 000 0.00121602
## 84 85 UNINCORPORATED UNI 000 0.00475766
## 86 87 UNINCORPORATED UNI 000 0.02893888
## 87 88 UNINCORPORATED UNI 000 0.00119324
## 89 90 UNINCORPORATED UNI 000 0.00104069
## 92 93 UNINCORPORATED UNI 000 0.01620425
## 93 94 UNINCORPORATED UNI 000 0.02632731
## 111 112 UNINCORPORATED UNI 000 7.66698069
## 113 114 UNINCORPORATED UNI 000 0.12734241
## 123 124 UNINCORPORATED UNI 000 55.28782437
## 125 126 UNINCORPORATED UNI 000 93.11464697
## last_edite GlobalID
## 1 2015-08-06T01:27:52.000Z {7DF0D7ED-3E1C-43B0-9F68-76A48D74F1CA}
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## 92 2015-08-06T01:27:51.000Z {7FDE214A-9BC1-4186-B843-3CA5D6837234}
## 93 2015-08-06T01:27:51.000Z {D1F3F237-7E94-4C2E-BD12-93D6130913C3}
## 111 2016-07-11T19:02:27.000Z {C775095D-D5F9-427F-AC37-A66E456FB75A}
## 113 2015-08-06T01:27:51.000Z {8BFDF727-2BF2-400B-9963-91FE86F626FD}
## 123 2017-03-02T19:28:35.000Z {F14958A5-6E76-4772-BD74-E5722B543D00}
## 125 2017-03-06T17:58:35.000Z {DFCA9BB6-EE1B-4B50-A98D-AE1D94497F2C}mean(stlco@data[stlco@data$MUNI=="UNI",]$SQ_MILES)## [1] 4.771136summary(stlco@data[stlco@data$MUNI=="UNI",]$SQ_MILES)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00026 0.00218 0.01405 4.77100 0.19970 93.11000stlco@data[stlco@data$MUNI=="UNI"&stlco@data$SQ_MILES>=50,]## OBJECTID MUNICIPALI MUNI MUNICODE SQ_MILES
## 123 124 UNINCORPORATED UNI 000 55.28782
## 125 126 UNINCORPORATED UNI 000 93.11465
## last_edite GlobalID
## 123 2017-03-02T19:28:35.000Z {F14958A5-6E76-4772-BD74-E5722B543D00}
## 125 2017-03-06T17:58:35.000Z {DFCA9BB6-EE1B-4B50-A98D-AE1D94497F2C}summary(stlco@data[stlco@data$MUNI=="UNI"&stlco@data$SQ_MILES<50,]$SQ_MILES)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000263 0.001883 0.010990 0.687000 0.147500 7.667000# Explore a specific Municipality
unique(stlco@data$MUNICIPALI)## [1] BERKELEY UNINCORPORATED
## [3] CALVERTON PARK HAZELWOOD
## [5] FENTON EUREKA
## [7] MANCHESTER BEL-RIDGE
## [9] CLAYTON ST. JOHN
## [11] KINLOCH GLENDALE
## [13] EDMUNDSON OAKLAND
## [15] SUNSET HILLS BEL-NOR
## [17] WEBSTER GROVES NORWOOD COURT
## [19] NORMANDY MOLINE ACRES
## [21] BRECKENRIDGE HILLS WARSON WOODS
## [23] LADUE WOODSON TERRACE
## [25] PACIFIC CHAMP
## [27] RIVERVIEW DELLWOOD
## [29] BELLEFONTAINE NEIGHBORS BLACK JACK
## [31] GREENDALE WILBUR PARK
## [33] MACKENZIE WINCHESTER
## [35] ROCK HILL MAPLEWOOD
## [37] CRYSTAL LAKE PARK HUNTLEIGH
## [39] COUNTRY LIFE ACRES BRENTWOOD
## [41] RICHMOND HEIGHTS FRONTENAC
## [43] OLIVETTE VINITA TERRACE
## [45] WELLSTON HILLSDALE
## [47] HANLEY HILLS UPLANDS PARK
## [49] VELDA VILLAGE HILLS VINITA PARK
## [51] BEVERLY HILLS PINE LAWN
## [53] VELDA CITY BELLERIVE ACRES
## [55] GREEN PARK LAKESHIRE
## [57] BELLA VILLA GRANTWOOD VILLAGE
## [59] TWIN OAKS GLEN ECHO PARK
## [61] NORTHWOODS PASADENA HILLS
## [63] PASADENA PARK UNIVERSITY CITY
## [65] PAGEDALE COUNTRY CLUB HILLS
## [67] CHESTERFIELD SYCAMORE HILLS
## [69] CHARLACK OVERLAND
## [71] COOL VALLEY FERGUSON
## [73] FLORDELL HILLS JENNINGS
## [75] KIRKWOOD MARLBOROUGH
## [77] SHREWSBURY CLARKSON VALLEY
## [79] CRESTWOOD ST. ANN
## [81] DES PERES TOWN & COUNTRY
## [83] WESTWOOD CREVE COEUR
## [85] MARYLAND HEIGHTS BRIDGETON
## [87] ELLISVILLE BALLWIN
## [89] WILDWOOD FLORISSANT
## [91] VALLEY PARK
## 91 Levels: BALLWIN BEL-NOR BEL-RIDGE ... WOODSON TERRACEstlco[stlco@data$MUNICIPALI=="FERGUSON",]## An object of class "SpatialPolygonsDataFrame"
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## OBJECTID MUNICIPALI MUNI MUNICODE SQ_MILES last_edite
## 102 103 FERGUSON FER 022 6.159777 2015-12-30T19:09:14.000Z
## GlobalID
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## [901,] -90.30252 38.76807
## [902,] -90.30290 38.76849
## [903,] -90.30304 38.76864
## [904,] -90.30304 38.76864
## [905,] -90.30304 38.76865
## [906,] -90.30304 38.76865
## [907,] -90.30310 38.76871
## [908,] -90.30336 38.76899
## [909,] -90.30337 38.76901
## [910,] -90.30350 38.76915
## [911,] -90.30391 38.76960
## [912,] -90.30399 38.76969
## [913,] -90.30401 38.76972
## [914,] -90.30410 38.76981
## [915,] -90.30434 38.77008
## [916,] -90.30437 38.77012
## [917,] -90.30467 38.77046
## [918,] -90.30472 38.77052
## [919,] -90.30480 38.77048
## [920,] -90.30532 38.77019
## [921,] -90.30533 38.77019
## [922,] -90.30533 38.77019
## [923,] -90.30533 38.77018
## [924,] -90.30519 38.77003
## [925,] -90.30519 38.77002
## [926,] -90.30523 38.77000
## [927,] -90.30531 38.76996
## [928,] -90.30538 38.77005
## [929,] -90.30546 38.77014
## [930,] -90.30547 38.77016
## [931,] -90.30576 38.77050
## [932,] -90.30623 38.77104
## [933,] -90.30652 38.77138
## [934,] -90.30677 38.77168
## [935,] -90.30684 38.77176
## [936,] -90.30722 38.77220
## [937,] -90.30725 38.77224
## [938,] -90.30747 38.77250
## [939,] -90.30751 38.77254
## [940,] -90.30623 38.77242
## [941,] -90.30422 38.77221
## [942,] -90.30269 38.77205
## [943,] -90.30119 38.77189
## [944,] -90.30088 38.77186
## [945,] -90.30084 38.77186
## [946,] -90.30048 38.77182
## [947,] -90.29945 38.77172
## [948,] -90.29908 38.77169
## [949,] -90.29901 38.77169
## [950,] -90.29821 38.77162
## [951,] -90.29760 38.77158
## [952,] -90.29655 38.77151
## [953,] -90.29606 38.77148
## [954,] -90.29487 38.77142
## [955,] -90.29464 38.77142
## [956,] -90.29389 38.77139
## [957,] -90.29380 38.77139
## [958,] -90.29328 38.77137
## [959,] -90.29288 38.77136
## [960,] -90.29159 38.77133
## [961,] -90.28826 38.77125
## [962,] -90.28498 38.77117
## [963,] -90.28397 38.77115
## [964,] -90.28165 38.77110
## [965,] -90.27998 38.77106
## [966,] -90.27996 38.77106
## [967,] -90.27948 38.77105
## [968,] -90.27936 38.77104
## [969,] -90.27935 38.77104
## [970,] -90.27925 38.77104
## [971,] -90.27839 38.77102
## [972,] -90.27713 38.77099
## [973,] -90.27523 38.77095
## [974,] -90.27474 38.77094
## [975,] -90.27193 38.77090
## [976,] -90.27140 38.77090
## [977,] -90.27039 38.77088
## [978,] -90.26946 38.77087
## [979,] -90.26941 38.77087
## [980,] -90.26877 38.77087
## [981,] -90.26803 38.77086
## [982,] -90.26711 38.77085
## [983,] -90.26688 38.77084
## [984,] -90.26608 38.77083
## [985,] -90.26565 38.77083
## [986,] -90.26512 38.77082
## [987,] -90.26230 38.77078
## [988,] -90.26058 38.77076
## [989,] -90.26058 38.77076
## [990,] -90.26056 38.77076
## [991,] -90.26056 38.77076
## [992,] -90.26054 38.77066
## [993,] -90.26053 38.77061
##
##
##
## Slot "plotOrder":
## [1] 1
##
## Slot "labpt":
## [1] -90.29501 38.74890
##
## Slot "ID":
## [1] "102"
##
## Slot "area":
## [1] 0.00165345
##
##
##
## Slot "plotOrder":
## [1] 1
##
## Slot "bbox":
## min max
## x -90.31909 -90.25912
## y 38.72506 38.77254
##
## Slot "proj4string":
## CRS arguments:
## +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0plot(stlco)
plot(stlco[stlco@data$MUNICIPALI=="FERGUSON",], col="green", add = TRUE)
Compute centroid of municipality
The centroid or center of mass is one way to represent the “middle” of a given polygon.
fergcent<-gCentroid(stlco[stlco@data$MUNICIPALI=="FERGUSON",])
fergcent## SpatialPoints:
## x y
## 1 -90.29501 38.7489
## Coordinate Reference System (CRS) arguments: +proj=longlat
## +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0plot(stlco)
plot(stlco[stlco@data$MUNICIPALI=="FERGUSON",], col="green", add = TRUE)
plot(fergcent,add = T,col="red")
Compute a buffer
A buffer is a region that extends a spatial object outward by a given distance. A buffer around a point is a circle centered at that point. A buffer of a street centerline is a strip of land that could include the shoulder, the median, and sidewalk running parallel to the road. A buffer of a state could include all regions within 10 miles of that state’s border.
We note here that the data from St. Louis County was delivered in geographic coordinates, not projected coordinates. This means that we have latitude and longitude data that is inherently 3D. We will need to project this in order to compute a buffer. We will first try to only project the centroid, compute the buffer relative to it, and continue from there.
ferg_cent_MOstplE<-spTransform(fergcent, CRS("+proj=tmerc +lat_0=35.83333333333334 +lon_0=-90.5 +k=0.999933333 +x_0=250000 +y_0=0 +ellps=GRS80 +units=m +no_defs"))
ferg_buffer <- gBuffer(spgeom = ferg_cent_MOstplE, width = 4828) # 3 mi ~ 4828 m
ferg_buffer## An object of class "SpatialPolygons"
## Slot "polygons":
## [[1]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] 267818.9 323577.2
##
## Slot "area":
## [1] 72030576
##
## Slot "hole":
## [1] FALSE
##
## Slot "ringDir":
## [1] 1
##
## Slot "coords":
## x y
## [1,] 272646.9 323577.2
## [2,] 272410.6 322085.3
## [3,] 271724.9 320739.4
## [4,] 270656.7 319671.3
## [5,] 269310.9 318985.5
## [6,] 267818.9 318749.2
## [7,] 266327.0 318985.5
## [8,] 264981.1 319671.3
## [9,] 263913.0 320739.4
## [10,] 263227.2 322085.3
## [11,] 262990.9 323577.2
## [12,] 263227.2 325069.2
## [13,] 263913.0 326415.1
## [14,] 264981.1 327483.2
## [15,] 266327.0 328168.9
## [16,] 267818.9 328405.2
## [17,] 269310.9 328168.9
## [18,] 270656.7 327483.2
## [19,] 271724.9 326415.1
## [20,] 272410.6 325069.2
## [21,] 272646.9 323577.2
##
##
##
## Slot "plotOrder":
## [1] 1
##
## Slot "labpt":
## [1] 267818.9 323577.2
##
## Slot "ID":
## [1] "buffer"
##
## Slot "area":
## [1] 72030576
##
##
##
## Slot "plotOrder":
## [1] 1
##
## Slot "bbox":
## min max
## x 262990.9 272646.9
## y 318749.2 328405.2
##
## Slot "proj4string":
## CRS arguments:
## +proj=tmerc +lat_0=35.83333333333334 +lon_0=-90.5 +k=0.999933333
## +x_0=250000 +y_0=0 +ellps=GRS80 +units=m +no_defsferg_buffer_geog_coords<- spTransform(ferg_buffer, CRSobj = stlcoCRS)
plot(stlco)
plot(stlco[stlco@data$MUNICIPALI=="FERGUSON",], col="green", add = TRUE)
plot(fergcent,add = T,col="red")
plot(ferg_buffer_geog_coords, add = T, border="blue")
Subsetting by a buffer
Computing a buffer allows you to easily subset the spatial objects within that buffer. (But you need the CRS to be the same.)
ferg_3mi <- stlco[ferg_buffer_geog_coords,]
plot(stlco)
plot(ferg_3mi, col = "lightblue", add = T)
plot(ferg_buffer_geog_coords, add = T, border="blue", lwd = 3)
plot(stlco[stlco@data$MUNICIPALI=="FERGUSON",], col="green", add = TRUE)
plot(fergcent,add = T,col="red")
Add in basemap with Leaflet
Now that St. Louis County Data is natively in geographic coordinates (lat/lon pairs), we can easily display it with Leaflet.
leaflet(stlco) %>% addTiles() %>% addPolygons() Other resources and data sources
CRAN Task View Spatial
A description of many useful packages is available in the Spatial Task View on CRAN: https://CRAN.R-project.org/view=Spatial.
Robin Lovelace et. al. - tutorial on visualizing spatial data in R
PDF available at: http://spatial.ly/wp-content/uploads/2013/12/intro-spatial-rl-3.pdf.
City of St. Louis data
Shapefiles from the city of St. Louis may be downloaded here: http://data.stlouis-mo.gov/downloads.cfm.
Mapping National Parks
You can obtain a shapefile of National Park Boundaries from https://irma.nps.gov/DataStore/Reference/Profile/2224545?lnv=True.